Git for Data Engineers: Unlock Version Control Foundations in 10 Minutes

From zero to productive: an easy-to-follow guide to understanding Git’s core concepts and applying them to real-world data engineering projects.

Check my Git Playlist to read more about using Git in Data Engineering.

Greetings, curious reader,

When I started writing my first lines of code, even before stepping into data engineering, we used an FTP server to host ZIP files with versions of our code.

Each ZIP file had a timestamp or a version number in its name, like project_v1.zip, project_final.zip, or even project_final_final_v2.zip.

If someone needed to make a change, they would download the latest ZIP file, edit it, and then upload a new version to the server.

It didn’t take long for problems to appear.

What if two people downloaded the same ZIP file and made changes at the same time? The first person to upload their new version would overwrite the other person’s work.

As projects got more complex, trying to version these files with ZIP archives or manual file naming systems became a nightmare.

That’s when my coworker and I (yes, we were a team of two) discovered Git.

With Git, you can track every change you make to your code, experiment without affecting the main version, and collaborate with others. Instead of juggling ZIP files or worrying about overwriting someone’s work, Git gives you a structured way to organise your code, undo mistakes, and ensure your projects run.

In this article, I’ll walk you through Git’s core concepts, explain how it works under the hood, and show you how to use it for real-world data engineering projects. Enjoy!

🔗 Why Git Matters for Data Engineers

The Current State 🛠️

In data engineering, collaboration is the norm. Even if you are a team of one, you’re often part of a team, working alongside other people.

99% of your tasks involve editing shared codebases—sometimes with multiple people working on the same files at the same time. Without a version control system like Git, this kind of collaboration becomes chaotic.

For example, imagine you’re fine-tuning a Python script that automates data ingestion. At the same time, a colleague is implementing a new feature in the same script. You could overwrite their work without a clear way to track your changes. Worse, if you delete a critical part of the code, you might not even realise it until it’s too late to recover.

This is why Git has become a must-have skill in the data engineering toolkit.

Just think of it: there are over 100 million people on GitHub.

It allows teams to collaborate seamlessly, ensures accountability by tracking who made what changes, and provides a safety net by enabling you to roll back to earlier versions of your code.

The Stakes 🔥

Let’s break the stakes down:

1. 🤝 Collaboration Breakdowns: Without Git, teamwork becomes difficult. Code can be accidentally overwritten, or changes can conflict with one another, leading to hours wasted manually reconciling issues.

2. 🗑️ Lost Work: Data pipelines, infrastructure scripts, and SQL queries evolve over time. If you don’t use Git to track changes, there’s no way to recover a working version if something breaks.

3. 🐞 Debugging Nightmares: Let’s say your data pipeline suddenly starts failing. Without a clear history of changes, tracing the source of the problem can be a time-consuming, frustrating process. Git’s history log can save hours by showing when a breaking change was introduced.

By mastering Git, you set yourself up for success. You’ll collaborate more effectively, save time debugging, and confidently handle larger, more complex projects.

🔍 Deep Dive in Core Concepts

What is Git ❓

Git is a distributed version control system. This means it tracks changes to your code and saves snapshots of your project at different points in time. These snapshots, or save points if you wish, allow you to revert to any previous version of your project if something goes wrong.

But Git goes far beyond basic versioning. It’s designed for teams, enabling multiple contributors to work on the same project simultaneously without overwriting each other’s work.

Git is fast, efficient, and flexible. It provides a structured way for data engineers to manage pipelines, scripts, and configurations. It gives you confidence that you can adapt to changes quickly without risking production environments.

Core Concepts of Git 🌳

To get started with Git, you need to understand some key concepts:

1. Repository (Repo): A repository is a folder where Git tracks changes to your files. It contains all the history and metadata needed to manage your project.

2. Commit: A commit is a snapshot of your project at a specific point in time. Think of it as hitting “save” in Git. Each commit includes a description (written by you) explaining what was changed and why.

3. Branch: A branch is like a parallel version of your project. It allows you to experiment with new features or fixes without affecting the main codebase. Once your changes are ready, you can merge your branch back into the main branch.

4. Merge: Merging combines the changes from one branch into another. This is typically done after reviewing and testing new features or fixes.

5. Push and Pull: These commands sync your local work with a remote repository. Pushing sends your changes to the remote repository (e.g., GitHub) while pulling fetches updates made by others.

6. Staging Area: Before committing changes, you add them to the staging area. This step lets you decide which changes to include in the next snapshot.

By understanding these concepts, you’ll have the foundation to start using Git effectively.

⚙️ How Git Works Under the Hood